Optimal ultrasound-based organ segmentation

ABSTRACT

A segmentation selection system includes a transducer configured to transmit and receive imaging energy for imaging a subject. A signal processor is configured to process imaging data received to generate processed image data. A segmentation module is configured to generate a plurality of segmentations of the subject based on features or combinations of features of the imaging data and/or the processed image data. A selection mechanism is configured to select one of the plurality of segmentations that best meets a criterion for performing a task. A graphical user interface permits a user to select features or combinations of features of imaging data or processed image data to generate the plurality of segmentations and to select a segmentation that best meets criterion for performing a task.

CROSS-REFERENCE TO PRIOR APPLICATIONS

This application is a divisional application of U.S. patent applicationSer. No. 15/560,055, filed on Sep. 20, 2017, which in turn is the U.S.National Phase application under 35 U.S.C. § 371 of InternationalApplication No. PCT/IB2016/051454, filed on Mar. 15, 2016, which claimsthe benefit of U.S. Provisional Patent Application No. 62/138,075, filedon Mar. 25, 2015, and U.S. Provisional Patent Application No.62/169,577, filed on Jun. 2, 2015. These applications are herebyincorporated by reference herein.

BACKGROUND Technical Field

This disclosure relates to medical instruments and more particularly tosystems, methods and user interfaces for image component segmentationfor imaging applications.

Description of the Related Art

Ultrasound (US) is a low-cost, easy-to-use imaging modality that iswidely used for intra-procedural real-time guidance and treatmentmonitoring. Ultrasound image segmentation is mainly driven by theclinical need for extracting organ boundaries in B-mode images as a steptowards dimension measurement (e.g., tumor size and extent). Theappearance of geometric boundaries of organs in US images mainly dependson the acoustic impedance between tissue layers. Despite its low costand ease-of-use, US B-mode imaging is not necessarily the most suitablefor anatomical imaging. B-mode US images, compared to other imagingmodalities, such as, magnetic resonance (MR) and computed tomography(CT), suffer from poor signal-to-noise ratio and background specklenoise. Existing ultrasound-based segmentation methods utilize pixel (orvoxel, in 3D) information from the B-mode images as input to a metriccalculator (where ‘metric’ refers to the quantity calculated, namely,signal-to-noise ratio (SNR), contrast, texture etc.).

US B-mode images are also afflicted with the problem of poor contrastbetween an organ and its immediately surrounding tissue (e.g.,prostate-bladder, prostate-rectal wall) due to isoechoic pixel values onthe B-mode images. This limits the robustness of many existing automatedsegmentation methods. Manual segmentation remains the only possiblealternative, and the achievable accuracy with this method is heavilydependent on the skill-level of the clinician. The inter-operatorvariability in manual US segmentation is on the order of 5 mm, with theDice coefficient (a measure of similarity) being 20-30% lower than forMRI segmentations.

SUMMARY

In accordance with the present principles, a segmentation selectionsystem includes a transducer configured to transmit and receive imagingenergy for imaging a subject. A signal processor is configured toprocess imaging data received to generate processed image data. Asegmentation module is configured to generate a plurality ofsegmentations of the subject based on features or combinations offeatures of the imaging data and/or the processed image data. Aselection mechanism is configured to select one of the plurality ofsegmentations that best meets a criterion for performing a task.

Another segmentation selection system includes an ultrasound transducerconfigured to transmit and receive ultrasound energy for imaging asubject. A B-mode processor is configured to process imaging datareceived to generate processed image data. A segmentation module isconfigured to generate a plurality of segmentations of the subject basedon one or more combinations of input data and segmentation metrics. Agraphical user interface permits a user to select features orcombinations of features of imaging data and/or processed image data togenerate the plurality of segmentations and to select a segmentationthat best meets criterion for performing a task.

A method for segmentation selection includes receiving imaging energyfor imaging a subject; image processing data received to generateprocessed image data; generating a plurality of segmentations of thesubject based on features or combinations of features of raw imagingdata and/or processed imaging data; and selecting at least one of theplurality of segmentations that best meets a segmentation criterion.

These and other objects, features and advantages of the presentdisclosure will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

This disclosure will present in detail the following description ofpreferred embodiments with reference to the following figures wherein:

FIG. 1 is a block/flow diagram showing an imaging system with asegmentation selection module in accordance with one embodiment;

FIG. 2 is a block/flow diagram showing data or signal inputs to asegmentation selection module for generating a plurality ofsegmentations in accordance with one embodiment;

FIG. 3 is a diagram showing an illustrative graphical user interface forproviding selection criteria and selecting a segmentation in accordancewith one embodiment; and

FIG. 4 is a flow diagram showing a segmentation selection method inaccordance with illustrative embodiments.

DETAILED DESCRIPTION OF EMBODIMENTS

In accordance with the present principles, systems and methods areprovided to estimate an optimal segmentation from multiple segmentationsof a single imaging modality (e.g., ultrasound (US)). The multiplesegmentations may employ, e.g., raw beam-summed US radiofrequency (RF)data and/or other data matrices along the pipeline of B-mode (brightnessmode) image formation, in conjunction with various segmentation metrics.Both manual and automatic methods may be selectable by a user through auser interface to choose the optimal segmentation.

Ultrasound B-mode images are created by detecting an envelope of raw RFdata, followed by logarithmic compression and scan conversion. Thelogarithmic data compression permits concurrent visualization of a widerange of echoes. However, this also suppresses subtle variations inimage contrast that may be vital to achieving an accurate segmentation.In accordance with the present principles, a computation of segmentationmetrics is provided on different US data forms (prior to the generationof conventional B-mode data). The segmentation will be derived from oneor more metrics (texture, contrast, etc.). Based on the data matrix andsegmentation metric used, multiple segmentation outputs will bepresented to the user, with the possibility of manually or automaticallychoosing the optimal segmentation. The chosen segmentation will besuperimposed on the B-mode image visualized on the screen, withoutadding any complexity from the user's perspective. Overall, theunprocessed RF data is potentially rich in information about the organunder investigation and the boundaries between tissues. Involvingseveral different data streams in the segmentation process,complementary to the B-mode image, can lead to a more accuratesegmentation that is less sensitive to speckle noise. Hence, thecomputation of multi-channel segmentation metrics on pre-log compresseddata is provided. The present principles increase the accuracy androbustness of segmentation algorithms, thereby improving andstreamlining clinical workflow.

It should be understood that the present invention will be described interms of medical instruments for US imaging; however, the teachings ofthe present invention are much broader and are applicable to any imagingmodality where multiple segmentation options can be provided for thatmodality. In some embodiments, the present principles are employed intracking or analyzing complex biological or mechanical systems. Inparticular, the present principles are applicable to internal trackingprocedures for biological systems, and may include procedures in allareas of the body such as the lungs, gastro-intestinal tract, excretoryorgans, blood vessels, etc. The elements depicted in the FIGS. may beimplemented in various combinations of hardware and software and providefunctions which may be combined in a single element or multipleelements.

The functions of the various elements shown in the FIGS. can be providedthrough the use of dedicated hardware as well as hardware capable ofexecuting software in association with appropriate software. Whenprovided by a processor, the functions can be provided by a singlededicated processor, by a single shared processor, or by a plurality ofindividual processors, some of which can be shared. Moreover, explicituse of the term “processor” or “controller” should not be construed torefer exclusively to hardware capable of executing software, and canimplicitly include, without limitation, digital signal processor (“DSP”)hardware, read-only memory (“ROM”) for storing software, random accessmemory (“RAM”), non-volatile storage, etc.

Moreover, all statements herein reciting principles, aspects, andembodiments of the invention, as well as specific examples thereof, areintended to encompass both structural and functional equivalentsthereof. Additionally, it is intended that such equivalents include bothcurrently known equivalents as well as equivalents developed in thefuture (i.e., any elements developed that perform the same function,regardless of structure). Thus, for example, it will be appreciated bythose skilled in the art that the block diagrams presented hereinrepresent conceptual views of illustrative system components and/orcircuitry embodying the principles of the invention. Similarly, it willbe appreciated that any flow charts, flow diagrams and the likerepresent various processes which may be substantially represented incomputer readable storage media and so executed by a computer orprocessor, whether or not such computer or processor is explicitlyshown.

Furthermore, embodiments of the present invention can take the form of acomputer program product accessible from a computer-usable orcomputer-readable storage medium providing program code for use by or inconnection with a computer or any instruction execution system. For thepurposes of this description, a computer-usable or computer readablestorage medium can be any apparatus that may include, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device) or apropagation medium. Examples of a computer-readable medium include asemiconductor or solid state memory, magnetic tape, a removable computerdiskette, a random access memory (RAM), a read-only memory (ROM), arigid magnetic disk and an optical disk. Current examples of opticaldisks include compact disk—read only memory (CD-ROM), compactdisk—read/write (CD-R/W), Blu-Ray™ and DVD.

Reference in the specification to “one embodiment” or “an embodiment” ofthe present principles, as well as other variations thereof, means thata particular feature, structure, characteristic, and so forth describedin connection with the embodiment is included in at least one embodimentof the present principles. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment”, as well any other variations,appearing in various places throughout the specification are notnecessarily all referring to the same embodiment.

It is to be appreciated that the use of any of the following “/”,“and/or”, and “at least one of”, for example, in the cases of “A/B”, “Aand/or B” and “at least one of A and B”, is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of both options (A andB). As a further example, in the cases of “A, B, and/or C” and “at leastone of A, B, and C”, such phrasing is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of the third listedoption (C) only, or the selection of the first and the second listedoptions (A and B) only, or the selection of the first and third listedoptions (A and C) only, or the selection of the second and third listedoptions (B and C) only, or the selection of all three options (A and Band C). This may be extended, as readily apparent by one of ordinaryskill in this and related arts, for as many items listed.

Referring now to the drawings in which like numerals represent the sameor similar elements and initially to FIG. 1, an ultrasound imagingsystem 10 constructed in accordance with the present principles is shownin block diagram form. The ultrasound system 10 includes a transducerdevice or probe 12 having a transducer array 14 for transmittingultrasonic waves and receiving echo information. The transducer arraymay be configured as, e.g., linear arrays or phased arrays, and caninclude piezoelectric elements or capacitive micromachined ultrasonictransducers (CMUT) elements. The transducer array 14, for example, caninclude a two dimensional array of transducer elements capable ofscanning in both elevation and azimuth dimensions for 2D and/or 3Dimaging.

The transducer array 14 is coupled to a microbeamformer 16 in the probe12, which controls transmission and reception of signals by thetransducer elements in the array. The microbeamformer 16 may beintegrated with the flexible transducer device 12 and is coupled to atransmit/receive (T/R) switch 18, which switches between transmissionand reception and protects a main beamformer 22 from high energytransmit signals. In some embodiments, the T/R switch 18 and otherelements in the system can be included in the transducer probe ratherthan in a separate ultrasound system base. The transmission ofultrasonic beams from the transducer array 14 under control of themicrobeamformer 16 is directed by a transmit controller 20 coupled tothe T/R switch 18 and the beamformer 22, which may receive input fromthe user's operation of a user interface or control panel 24.

One function controlled by the transmit controller 20 is the directionin which beams are steered. Beams may be steered straight ahead from(orthogonal to) the transducer array 14, or at different angles for awider field of view. The partially beamformed signals produced by themicrobeamformer 16 are coupled to a main beamformer 22 where partiallybeamformed signals from individual patches of transducer elements arecombined into a fully beamformed signal.

The beamformed signals are coupled to a signal processor 26. The signalprocessor 26 can process the received echo signals in various ways, suchas bandpass filtering, decimation, I and Q component separation, andharmonic signal separation. The signal processor 26 may also performadditional signal enhancement such as speckle reduction, signalcompounding, and noise elimination. The processed signals are coupled toa B mode processor 28 or other mode processor, e.g., an M-mode processor29), which can employ amplitude detection for the imaging of structuresin the body. The signals produced by the mode processors 28, 29 arecoupled to a scan converter 30 and a multiplanar reformatter 32. Thescan converter 30 arranges the echo signals in the spatial relationshipfrom which they were received in a desired image format. For instance,the scan converter 30 may arrange the echo signal into a two dimensional(2D) sector-shaped format, or a pyramidal three dimensional (3D) image.The multiplanar reformatter 32 can convert echoes which are receivedfrom points in a common plane in a volumetric region of the body into anultrasonic image of that plane.

A volume renderer 34 converts the echo signals of a 3D data set into aprojected 3D image as viewed from a given reference point. The 2D or 3Dimages are coupled from the scan converter 30, multiplanar reformatter32, and volume renderer 34 to an image processor 36 for furtherenhancement, buffering and temporary storage for display on an imagedisplay 38. A graphics processor 40 can generate graphic overlays fordisplay with the ultrasound images. These graphic overlays or parameterblocks can contain, e.g., standard identifying information such aspatient name, date and time of the image, imaging parameters, frameindices and the like. For these purposes, the graphics processor 40receives input from the user interface 24, such as a typed patient name.The user interface 24 can also be coupled to the multiplanar reformatter32 for selection and control of a display of multiple multiplanarreformatted (MPR) images.

In accordance with the present principles, ultrasound data is acquiredand stored in memory 42. The memory 42 is depicted as being centrallyplaced; however, the memory 42 may store data and interact at anyposition in the signal path. Corrections may be employed as feedback forcorrecting a beam steering signal (Beam Steer) in accordance with thepositions of the elements in the array 14.

Display 38 is included for viewing internal images of a subject(patient) or volume. Display 38 may also permit a user to interact withthe system 10 and its components and functions, or any other elementwithin the system 10. This is further facilitated by the interface 24,which may include a keyboard, mouse, a joystick, a haptic device, or anyother peripheral or control to permit user feedback from and interactionwith the system 10.

Ultrasound B-mode images are output from the B-mode processor 28 and arecreated by detecting an envelope of raw RF data, followed by logarithmiccompression and scan conversion by the scan converter 30. Thelogarithmic data compression by the B-mode processor 28 permitsconcurrent visualization of a wide range of echoes.

Ultrasound M-mode images are output from an M-mode processor 29. M-modeimages may be processed (e.g., compressed) and sent for scan conversionby the scan converter 30. In M-mode (motion mode) ultrasound, pulses areemitted in quick succession when A-mode or B-mode images are taken torecord successive images. As the organ boundaries produce reflectionsrelative to the probe 12, this can be used to determine the velocity ofspecific organ structures.

A computation of segmentation metrics is provided on different US dataforms (prior to the generation of conventional B-mode data or M-modedata) and received by a segmentation module 50. The segmentation module50 derives a plurality of segmentation images generated from one or moremetrics (e.g., texture, contrast, etc.) measured from the US data. Basedon the data matrix and segmentation metric employed, multiplesegmentation outputs will be generated using the image processor 36 (orgraphics processor 40) for display on the display 38. The segmentationoutputs are presented to the user on a graphical user interface (GUI) 52generated by the image processor 36 (or the graphics processor 40).

The segmentation outputs are presented to the user with the possibilityof manually or automatically choosing an optimal segmentation. Thechosen segmentation may be superimposed on the B-mode image visualizedon the display 38 by the image processor 36 (or the graphics processor40). The unprocessed RF data provided prior to B-mode processing 28 (oreven before signal processing 26) is potentially rich in informationabout an organ or region under investigation and boundaries betweentissues. Introducing several different data types into the segmentationprocess by the segmentation module 50 can be complementary to the B-modeimage from the B-mode processor 28 and can lead to a more accuratesegmentation that is less sensitive to speckle noise or other imagedegradation phenomena. Hence, the computation of multi-channelsegmentation metrics on pre-log compressed data can increase theaccuracy and robustness of segmentation algorithms, thereby improvingand streamlining clinical workflow.

The image processor 36 is configured to generate a graphical userinterface (GUI) or other selection mechanism 52 for user selection of anoptimal segmentation. The segmentation module 50 provides differentsegmentation outputs resulting from multiple combinations of input dataand segmentation metrics to determine an optimal segmentation for anapplication at hand. Optimality can change based on the organ beingsegmented and the task at hand. Input data refers to the use ofdifferent forms of US data in segmentation algorithms and include butare not limited to the following potential possibilities, e.g., rawbeam-summed RF data, prior to envelope detection and logarithmiccompression, envelope-detected data, formed from raw beam-summed RFdata, B-mode images, formed without logarithmic compression, B-modeimages, formed without logarithmic compression and without applicationof other filters, conventional B-mode images, M-mode images, etc.Segmentation metrics refer to a quantity used to characterize tissueregions, e.g., Signal-to-Noise Ratio (SNR), contrast, texture,model-driven algorithms, etc. In some cases, optimality can be asubjective measure that is decided by the user.

The different segmentations obtained will then be presented to the useron a display 38 through, e.g., GUI 52, for manual selection of theoptimal segmentation. Automatic selection of the optimal segmentation isalso contemplated as will be described. The image processor 36 (orgraphics processor 40) provides visualizations of the segmentations. Thedifferent segmentation outputs are superimposed on the original B-modeimage, to permit the user to choose the best segmentation. In oneembodiment, the image processor 36 automatically cycles throughdifferent segmentation results periodically. In another embodiment, alloutputs may concurrently be visualized in a color-coded or other visualformat. If automatic selection of the optimal segmentation is performed,the selected optimal segmentation will be superimposed on the B-modeimage shown on the display 38.

Referring to FIG. 2, a block/flow diagram illustratively shows two setsof inputs to the segmentation module 50 where input data 102 andsegmentation metrics 104 are depicted. Inputs to the segmentation module50 show a pipeline of US B-mode image formation where uncompressed datais employed at various stages of the pipeline. A specific combinationinput data 102 and segmentation metrics 104 will provide the ‘optimal’segmentation for a particular organ site, US system capabilities, etc.The input data 102 may include raw RF data 106 (from anywhere in thesignal path prior to the B-mode processor 28, FIG. 1), envelopedetection data 108 (from signal envelope (carrier waves)) and from aB-mode display 114 (B-mode images or image data). M-mode images or data115 (or other image modes) may also be employed as input data. TheB-mode or M-mode images may be derived from a scan conversion 112 withor without logarithmic compression 110. The segmentation metrics 104 mayinclude statistical models 116 or the US imaged volume, SNR 118,contrast 120, texture 122, edge detection 124 or any other imagecharacteristics.

For the input data 102, the logarithmic compression of the data used isavoided to employ the entire range of the information captured forcalculation of the segmentation metric 104. Multiple segmentations maybe provisioned from which the ‘optimal’ or ‘best’ segmentation can beselected by the segmentation module 50. This selection can be madeautomatically or manually.

For an automatic selection of the optimal segmentation, appropriatecriteria may be defined. Examples of potential criteria that may be usedinclude, e.g., Whether the segmented volume is at least ‘x’ cm³?; Doesthe segmented volume include certain pre-annotated anatomicallandmarks?; Does the segmented volume differ from the meanpopulation-based segmentation model by greater than ‘x’ %?; Does thesegmented shape differ from the mean population-based shape model bygreater than ‘x’ %?; etc. The metrics that are not utilized to generatethe segmentation may be utilized to rate the quality of thesegmentation.

For a manual selection of the optimal segmentation, the differentsegmentations can be presented to the user by superimposing them on theB-mode image(s), e.g., all segmentations may be superimposed, each witha different color/line style, a single segmentation can be superimposedat any given time, with the user having the ability to cycle through allthe available segmentations through mouse clicks and/or keyboardshortcuts.

Referring to FIG. 3, an illustrative GUI 52 is shown in accordance withone exemplary embodiment. GUI 52 includes an image panel 202 where animage may include superimposed candidate segmentations 204, 206. Eachcandidate segmentation 204, 206 may be shown superimposed on a B-modeimage 205. The candidate segmentations 204, 206 may all be shownconcurrently or sequentially (or any combination thereof). The user mayselect input factors such as an organ to be segmented in check boxes208, 210, 212, 214 and 216. The user may also select data on whichsegmentation is to be performed by selecting check boxes 218, 220, 222.The user may also select data on which segmentation metric is used toperform the segmentation by selecting check boxes 224, 226, 228. A finalsegmentation may be elected by presses button 230.

If the segmentations are performed in an automated manner, thesegmentations resulting from each combination of input factors are shownto the user for selection of the ‘optimal’ segmentation. If thesegmentations are to be manually performed by the user, the user issequentially shown multiple images (e.g., the beam-summed RF image, theenvelope detected image, B-mode images with and without filtering,etc.). The user performs a manual segmentation on the displayed imageand has the choice of accepting or rejecting it. The user may select theautomatic or manual modes of operation. After the optimal segmentationis selected by the user, it is finalized by clicking the ‘Finalizesegmentation’ button 230.

It should be understood that the GUI 52 depicted in FIG. 3 is forillustrative purposes. The interface may be developed or extended asneeded to include more functionality. The type and positioning offeatures on the GUI 52 may be changed or reorganized as needed ordesired. Additional buttons or controls may be employed or some buttonsor controls may be removed.

The present principles provide segmentation techniques, which findapplication in a variety of areas. Examples of accurate segmentationleading to accurate image registration find utility in areas such as,e.g., adaptive treatment planning for radiation therapy,intra-procedural therapy monitoring (e.g., brachytherapy, RF ablation),real-time biopsy guidance, etc. It should be understood that whiledescribed in terms of US imaging, the segmentation selection aspects inaccordance with the present principles may be employed for other imagingmodalities instead of US. For example, the segmentation selection may beemployed for MRI, CT or other imaging modalities.

The present embodiments may be employed to provide enhanced organsegmentation capabilities that complement tracking technologies (e.g.,EM tracking, ultrasound tracking) for interventional devices. Also, thesegmentation methods and user interface can be integrated in existingcommercial systems, without the need to provide user access to raw data.Automated segmentation capability in accordance with the presentprinciples may be employed with improved accuracy on any of theclinically-available imaging systems, and in particular US imagingsystems.

Referring to FIG. 4, a method for segmentation selection is shown inaccordance with illustrative embodiments. In block 302, imaging energyis received for imaging a subject. The imaging energy may includeultrasound, although other imaging modalities and energy types may beemployed. In block 304, the received data is image processed to generateprocessed image data. The processed image data may include logarithmiccompression, or other filtering or compression. The processing mayinclude scan converting the data and/or B-mode processing. Other formsof processing are also contemplated.

In block 306, a plurality of segmentations are generated for the subjectbased on features or combinations of features of raw imaging data and/orprocessed imaging data. The raw imaging data may include rawradiofrequency data, envelope detection data, signal to noise ratiodata, contrast data, texture data, edge detection data, etc. Theprocessed imaging data may include a statistical model comparison,compressed data, converted data, B-mode processed display data, etc.

Segmentations may be generated based on raw data and processed data indifferent combinations to generate segmentations that differ from oneanother. Generating the plurality of segmentations may includegenerating the plurality of segmentations based on one or morecombinations of input data and segmentation metrics. For example, onesegmentation may be generated using a particular segmentation metric anda particular type of input data. The input data may include raw RF data,envelope detection data, B-mode display data, etc. The segmentationmetric information may include statistical model comparison data, signalto noise data, contrast data, texture data, edge detection data, etc.Other segmentations may combine input data with segmentation metrics orcombined aspects of the input data with aspects of the segmentationmetrics. As an example, raw RF data may be combined with contrast andtexture data to generate a segmentation. Other combinations arecontemplated.

In block 308, at least one of the plurality of segmentations is selectedthat best meets a segmentation criterion. The selection criteria mayinclude use desired aspects or automatic criteria. The features orcombinations of features of the raw imaging data and/or the processedimaging data may be employed to generate the plurality of segmentations.The segmentation may be manually selected, which best meets user definedcriteria or automatically selected based on programmed criteria (forexample, contrast or pixel thresholds, a best fit image with astatistical model shape, etc.).

In block 310, a graphical user interface is generated and displays theplurality of segmentations. The segmentations are preferably displayedon a B-mode image (background), wherein the segmentation images aredisplayed concurrently or sequentially in accordance with a userpreference.

In block 312, the selected segmentation is employed to perform anoperative procedure or other task.

In interpreting the appended claims, it should be understood that:

-   -   a) the word “comprising” does not exclude the presence of other        elements or acts than those listed in a given claim;    -   b) the word “a” or “an” preceding an element does not exclude        the presence of a plurality of such elements;    -   c) any reference signs in the claims do not limit their scope;    -   d) several “means” may be represented by the same item or        hardware or software implemented structure or function; and    -   e) no specific sequence of acts is intended to be required        unless specifically indicated.

Having described preferred embodiments for optimal ultrasound-basedorgan segmentation (which are intended to be illustrative and notlimiting), it is noted that modifications and variations can be made bypersons skilled in the art in light of the above teachings. It istherefore to be understood that changes may be made in the particularembodiments of the disclosure disclosed which are within the scope ofthe embodiments disclosed herein as outlined by the appended claims.Having thus described the details and particularity required by thepatent laws, what is claimed and desired protected by Letters Patent isset forth in the appended claims.

1. A segmentation selection system, comprising: an ultrasound transducerconfigured to transmit and receive ultrasound energy for imaging asubject; a B-mode processor configured to process imaging data receivedto generate processed image data; a segmentation module configured togenerate a plurality of segmentations of the subject based on one ormore combinations of input data and segmentation metrics wherein thesegmentation metrics are derived from imaging data that has not beencompressed for image processing; and a graphical user interface thatpermits a user to select features or combinations of features of imagingdata and/or processed image data to generate the plurality ofsegmentations and to select a segmentation that best meets criterion forperforming a task.
 2. The system as recited in claim 1, wherein theinput data includes at least one of raw radiofrequency data, envelopedetection data or B-mode display data
 3. The system as recited in claim1, wherein the segmentation metrics include at least one of astatistical model signal to noise ratio data, contrast data, texturedata or edge detection data.
 4. The system as recited in claim 1,further comprising an image processor configured to automatically selecta segmentation that best meets the criterion for performing a task basedupon programmed criteria.
 5. The system as recited in claim 1, furthercomprising a display for displaying images of the plurality ofsegmentations, wherein the images are displayed one of concurrently orsequentially on a B-mode image.
 6. A method for segmentation selection,comprising: receiving imaging energy for imaging a subject; processingimage data received to generate processed image data; generating aplurality of segmentations of the subject based on features orcombinations of features of raw imaging data and/or processed imagingdata wherein the raw imaging data and/or processed imaging data includesat least one of raw radiofrequency data and envelope detection data; andselecting at least one of the plurality of segmentations that best meetsa segmentation criterion.
 7. The method as recited in claim 6, whereinselecting includes automatically selecting a segmentation based uponprogrammed criteria.